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TechRxiv 2023年

作者： Franco-Barranco, Daniel Lin, Zudi Jang, Won-Dong Wang, Xueying Shen, Qijia Yin, Wenjie Fan, Yutian Li, Mingxing Chen, Chang Xiong, Zhiwei Xin, Rui Liu, Hao Chen, Huai Li, Zhili Zhao, Jie Chen, Xuejin Pape, Constantin Conrad, Ryan De Folter, Jozefus Nightingale, Luke Jones, Martin L. Liu, Yanling Ziaei, Dorsa Huschauer, Stephan Arganda-Carreras, Ignacio Pfister, Hanspeter Wei, Donglai The Department of Computer Science and Artificial Intelligence University of the Basque Country Donostia-San Sebastian Spain San Sebastian Spain Ikerbasque Basque Foundation for Science Bilbao Spain Biofisika Institute CSIC UPV/EHU Bilbao Spain Harvard University All-ston MA United States The Department of Molecular and Cellular Biology Harvard University CambridgeMA United States The Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom University of Science and Technology of China Anhui China The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China The National Engineering Laboratory for Brain-inspired Intelligence Technology and Application University of Science and Technology of China Anhui China The Georg-August University Goettingen Germany The Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda United States The Cancer Research Technology Program Frederick National Laboratory for Cancer Research Frederick United States The Francis Crick Institute London United Kingdom The Advanced Biomedical Computational Science Group Frederick National Laboratory for Cancer Research FrederickMD United States The Computer Science Department Boston College Chestnut Hill MA United States

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 3,600 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that (1) the challenge metric was permissive with the false positive predictions and (2) the size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download. © 2023, CC BY.

关键词： Electron microscopy

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A Large-Scale Study of Failures on Petascale Supercomputers

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Journal of computer Science & technology 2018年第1期33卷 24-41页

作者： Rui-Tao Liu Zuo-Ning Chen State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 213215 China National Research Center of Parallel Computer Engineering and Technology Beijing 100190 China

With the rapid development of supercomputers, the scale and complexity are ever increasing, and the reliability and resilience are faced with larger challenges. There are many important technologies in fault tolerance, such as proacrive failure avoidance technologies based on fault prediction, reactive fault tolerance based on checkpoint, and scheduling technologies to improve reliability. Both qualitative and quantitative descriptions on characteristics of system faults are very critical for these technologies, This study analyzes the source of failures on two typical petascale supercomputers called Sunway BlueLight （based on multi-core CPUs） and Sunway TaihuLight （based on heterogeneous manycore CPUs）. It uncovers some interesting fault characteristics and finds unknown correlation relationship among main components＇ faults. Finally the paper analyzes the failure time of the two supercomputers in various grains of resource and different time spans, and builds a uniform multi-dimensional failure time model for petascale supereomputers.

关键词： petascale supercomputer fault characteristic correlation relationship multi-dimension failure time model

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Fully integrated Si-rich silicon nitride wavelength converter based on Bragg scattering intermodal four-wave mixing

Fully integrated Si-rich silicon nitride wavelength converte...

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49th European Conference on Optical Communications (ECOC 2023)

作者： V. Vitali T. D. Bucio H. Liu J. M. L. González F. Jurado-Romero A. Ortega-Moñux G. Churchill J. C. Gates J. Hillier N. Kalfagiannis D. Melati J. H. Schmid I. Cristiani P. Cheben J. G. Wangüuemert-Pérez Í. Molina-Fernández F. Gardes C. Lacava P. Petropoulos Optoelectronics Research Centre University of Southampton Southampton SO17 1BJ UK Telecommunication Research Institute (TELMA) Universidad de Málaga CEI Andalucía TECH E.T.S.I. Telecomunicación 29010 Málaga Spain School of Science and Technology Nottingham Trent University Nottingham NG11 8NS UK Centre de Nanosciences et de Nanotechnologies Université Paris-Saclay CNRS 91120 Palaiseau France Advanced Electronics and Photonics Research Center National Research Council Canada 1200 Montreal Road Ottawa ON K1A 0R6 Canada Electrical Computer and Biomedical Engineering Department University of Pavia Pavia 27100 Italy

We report the demonstration of an integrated silicon-rich silicon nitride wavelength converter based on Bragg scattering intermodal four-wave mixing process. The system integrates mode conversion, multiplexing and de-multiplexing functionalities on chip. Broadband signal conversion with a 3dB bandwidth exceeding 70 nm is demonstrated.

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Improved Damping Control Based on Hamiltonian-Energy Function with State-Observer for Permanent Magnet Synchronous Motor Drives

Improved Damping Control Based on Hamiltonian-Energy Functio...

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IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

作者： Thong-In Suyata Pongsiri Mungporn Burin Yodwong Matheepot Phattanasak Phatiphat Thounthong Ehsan Jamshidpour Serge Pierfederici Babak Nahid-Mobarakeh Nicu Bizon Ridtee Inteeworn Apinun Gonmanee Noureddine Takorabet Department of Teacher Training in Electrical Engineering Faculty of Technical Education King Mongkut’s University of Technology North Bangkok Bangkok Thailand ENSEM-GREEN Université de Lorraine Nancy France Thai French Innovation Institute King Mongkut’s University of Technology North Bangkok Bangkok Thailand Renewable Energy Research Centre King Mongkut’s University of Technology North Bangkok Bangkok Thailand Laboratoire d’Energetique et de Mecanique Théorique et Appliquée UMR 7563 CNRS University of Lorraine Vandoeuvre-lès-Nancy France Electrical and Computer Engineering McMaster University Hamilton ON Canada The National University of Science and Technology POLITEHNICA Bucharest Pitești University Centre Pitesti Romania Provincial Electricity Authority Nakhonnayok Nakhonnayok Thailand Department of Electronics Technology Khon Kaen Technical College Institute of Vocational Education Khon Kaen Thailand

This paper presents an original control algorithm using Hamiltonian energy control for a permanent magnet synchronous motor (PMSM) drive. The algorithm addresses the challenge of constant power load (CPL), which can cause instability of the input DC bus. The Hamiltonian energy control incorporates a model-based estimation to handle the CPL. An exponentially stable state-observer calculates load torque and stator resistance, improving control efficiency. Moreover, the difference flatness approach is used to generate a current reference to compensate for speed error in the nonlinear drive system. Experimental validation is conducted on a 10kW PMSM using a dSPACE MicroLabBox controller card. The Experimental results demonstrate the proposed algorithm's effectiveness, exhibiting excellent performance even with parameter variations.

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Classification of Human Sperm Head in Microscopic Images Using Twin Support Vector Machine and Neural Network 2nd

Classification of Human Sperm Head in Microscopic Images Usi...

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2nd International Conference on Innovative Computing and Communication, ICICC 2019

作者： Mohammed, Kamel K. Afify, Heba M. Fouda, Fayez Hassanien, Aboul Ella Bhattacharyya, Siddhartha Vaclav, Snasel Center for Studies and Research Viral Al Azhar University Cairo Egypt Electrical Engineering Department Egyptian Academy for Engineering and Advanced Technology Cairo Egypt Medical Biochemistry Department National Research Centre Cairo Egypt Faculty of Computers and Information Cairo University Giza Egypt RCC Institute of Information Technology Kolkata India Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic

ISBN: (纸本)9789811512858

This paper describes a computer-aided diagnosis (CAD) system for the classification of healthy/unhealthy human sperm heads based on texture analysis obtained from the microscopic images. This work is based on 60 human sperm microscopic images selected from the national research centre in Egypt. Texture analysis is done by using intensity histogram, Haralick’s Spatial Gray-Level Co-occurrence Matrix (SGLCM), statistical feature matrix (SFM), gray-level run length matrix (GLRLM), and invariant moment’s features. A total of forty-six texture features are computed for healthy as well as unhealthy region of interest (ROI). Since many numbers of the features may effect on accuracy of classification, optimal feature selection is done to eliminate the less informative and excessive features. The feature selection is adapted using twin support vector machine (TWSVM). The accuracy of these features in distinguishing healthy/unhealthy human sperm heads has been evaluated by back-propagation neural network (BPNN) and linear support vector machine (SVM) classifiers. From the results analysis, it was found that ANN classifier gave an overall classification accuracy of 100% with 100% sensitivity. The results show that it is feasible to identify human sperm heads based on texture features extracted from microscopic images. This CAD system is shown to be useful for increased accuracy and increased speed for the classification of healthy/unhealthy sperm heads for improving the fertilization process. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

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engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, ScienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

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SMILE-UHURA Challenge - Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms

arXiv

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arXiv 2024年

作者： Chatterjee, Soumick Mattern, Hendrik Dörner, Marc Sciarra, Alessandro Dubost, Florian Schnurre, Hannes Khatun, Rupali Yu, Chun-Chih Hsieh, Tsung-Lin Tsai, Yi-Shan Fang, Yi-Zeng Yang, Yung-Ching Huang, Juinn-Dar Xu, Marshall Liu, Siyu Ribeiro, Fernanda L. Bollmann, Saskia Chintalapati, Karthikesh Varma Radhakrishna, Chethan Mysuru Ram Kumar, Sri Chandana Hudukula Sutrave, Raviteja Qayyum, Abdul Mazher, Moona Razzak, Imran Rodero, Cristobal Niederer, Steven Lin, Fengming Xia, Yan Wang, Jiacheng Qiu, Riyu Wang, Liansheng Panah, Arya Yazdan Jurdi, Rosana El Fu, Guanghui Arslan, Janan Vaillant, Ghislain Valabregue, Romain Dormont, Didier Stankoff, Bruno Colliot, Olivier Vargas, Luisa Chacón, Isai Daniel Pitsiorlas, Ioannis Arbeláez, Pablo Zuluaga, Maria A. Schreiber, Stefanie Speck, Oliver Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Magdeburg Germany Department of Neurology Medical Faculty University Hospital of Magdeburg Magdeburg Germany German Centre for Neurodegenerative Diseases Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany University Hospital Zurich University of Zurich Zurich Switzerland Google Inc. United States Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexnder-Universität Erlangen-Nürnberg Erlangen Germany National Yang Ming Chiao Tung University Hsinchu Taiwan School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia Australian eHealth Research Centre Chile National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Computer Science and Engineering University of New South Wales Sydney Australia Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates The Alan Turing Institute London United Kingdom School of Computing University of Leeds Leeds United Kingdom Department of Computer Science School of Informatics Xiamen University Xiamen China Manteia Technologies Co. Ltd Xiamen China Leicester International Institute Dalian University of Technology Dalian China Sorbonne Université Institut du Cerveau - Paris Brain Institute ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié Salpêtrière ParisF-75013 France Centre of Formation and Research in Artificial Intelligence Universidad de Los Andes Colombia Data Science Department EURECOM Sophia Antipolis Franc

The human brain receives nutrients and oxygen through an intricate network of blood vessels. Pathology affecting small vessels, at the mesoscopic scale, represents a critical vulnerability within the cerebral blood supply and can lead to severe conditions, such as Cerebral Small Vessel Diseases. The advent of 7 Tesla MRI systems has enabled the acquisition of higher spatial resolution images, making it possible to visualise such vessels in the brain. However, the lack of publicly available annotated datasets has impeded the development of robust, machine learning-driven segmentation algorithms. To address the complexities of mesoscopic vessel segmentation and to highlight the need for advanced techniques to manage the high noise levels and poor vessel-to-background contrast inherent in"ultra-high-resolution" data, the SMILE-UHURA challenge was organised. This challenge, held in conjunction with the ISBI 2023, in Cartagena de Indias, Colombia, aimed to provide a platform for researchers working on related topics. The SMILE-UHURA challenge addresses the gap in publicly available annotated datasets by providing an annotated dataset of Time-of-Flight angiography acquired with 7T MRI. This dataset was created through a combination of automated pre-segmentation and extensive manual refinement. In this manuscript, sixteen submitted methods and two baseline methods are compared both quantitatively and qualitatively on two different datasets: held-out test MRAs from the same dataset as the training data (with labels kept secret) and a separate 7T ToF MRA dataset where both input volumes and labels are kept secret. The results demonstrate that most of the submitted deep learning methods, trained on the provided training dataset, achieved reliable segmentation performance. Dice scores reached up to 0.838 ± 0.066 and 0.716 ± 0.125 on the respective datasets, with an average performance of up to 0.804 ± 0.15. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Intelligent Emotion and Sensory Remote Prioritisation for Patients with Multiple Chronic Diseases

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Sensors 2023年第4期23卷 1854-1854页

作者： Alamoodi, A.H. Albahri, O.S. Zaidan, A.A. Alsattar, H.A. Zaidan, B.B. Albahri, A.S. Ismail, Amelia Ritahani Kou, Gang Alzubaidi, Laith Talal, Mohammed Perak Tanjong Malim35900 Malaysia Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya12733 Iraq SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia Department of Business Administration College of Administrative Science The University of Mashreq Baghdad10021 Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Baghdad10022 Iraq Department of Computer Science Kulliyyah of Information and Communication Technology International Islamic University Malaysia Kuala Lumpur53100 Malaysia School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics No. 555 Liutai Road Wenjiang District Chengdu611130 China School of Mechanical Medical and Process Engineering Queensland University of Technology BrisbaneQLD4000 Australia ARC Industrial Transformation Training Centre—Joint Biomechanics Queensland University of Technology BrisbaneQLD4000 Australia Batu Pahat86400 Malaysia

An intelligent remote prioritization for patients with high-risk multiple chronic diseases is proposed in this research, based on emotion and sensory measurements and multi-criteria decision making. The methodology comprises two phases: (1) a case study is discussed through the adoption of a multi-criteria decision matrix for high-risk level patients;(2) the technique for reorganizing opinion order to interval levels (TROOIL) is modified by combining it with an extended fuzzy-weighted zero-inconsistency (FWZIC) method over fractional orthotriple fuzzy sets to address objective weighting issues associated with the original TROOIL. In the first hierarchy level, chronic heart disease is identified as the most important criterion, followed by emotion-based criteria in the second. The third hierarchy level shows that Peaks is identified as the most important sensor-based criterion and chest pain as the most important emotion criterion. Low blood pressure disease is identified as the most important criterion for patient prioritization, with the most severe cases being prioritized. The results are evaluated using systematic ranking and sensitivity analysis. © 2023 by the authors.

关键词： Blood pressure

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Energy Systems Test Case Discovery Enabled by Test Case Profile and Repository

Energy Systems Test Case Discovery Enabled by Test Case Prof...

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Open Source Modelling and Simulation of Energy Systems (OSMSES)

作者： Petra Raussi Jirapa Kamsamrong Alexandros Paspatis Kai Heussen Tesfaye Amare Zerihun Edmund Widl Filip Pröstl Andrén Jawad H. Kazmi Thomas I. Strasser Felipe Castro Luigi Pellegrino Carbon Neutral Solutions VTT Techn. Research Centre of Finland Espoo Finland Energy Division OFFIS e.V. Oldenburg Germany School of Electrical and Computer Eng. National Technical University of Athens Athens Greece Wind and Energy Systems Technical University of Denmark Roskilde Denmark Energy Systems SINTEF Energy AS Trondheim Norway Center for Energy AIT Austrian Institute of Technology Vienna Austria Engineering Services SMA Solar Technology AG Kassel Germany Materials and Generation Technology Ricerca sul Sistema Energetico S.p.A. Milan Italy

Smart energy systems comprise multiple domains like power, thermal, control, information, and communication technology, which increases the complexity of research and development studies. This expansion also requires larger and ever so complex experimental pilot environments driving the demand for geographically distributed multi-research infrastructure tests. The Holistic Test Description approach supports the design of multi-domain and multi-research infrastructure tests by orga-nizing the test cases into comprehensive segments, ensuring all relevant items for testing are covered. These test cases eventually form a pool, which to understand holistically would require studying and reading all the descriptions. This work proposes therefore the concept of Test Case Profiles to improve test case discovery and the structured creation of them. Test Case Profiles add further structure to the indexing in test case repositories. Along with the proposed indexing method, four different use cases are introduced to motivate additional applications of the proposed concept.

关键词： Organizations Drives Benchmark testing Complexity theory Information and communication technology research and development Indexing

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Science education through project-based learning: A case study

Science education through project-based learning: A case stu...

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2022 International Conference on ENTERprise Information Systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2022

作者： Santos, Carolina Rybska, Eliza Klichowski, Michal Jankowiak, Barbara Jaskulska, Sylwia Domingues, Nuno Carvalho, Diana Rocha, Tânia Paredes, Hugo Martins, Paulo Rocha, João National School of Public Health-NOVA University of Lisbon Avenida Padre Cruz Lisbon1600-560 Portugal Comprehensive Health Research Centre NOVA University of Lisbon Lisbon Portugal Faculty of Biology-Adam Mickiewicz University Uniwersytetu Poznanskiego 6 Poznan61-614 Poland Faculty of Educational Studies-Adam Mickiewicz University Szamarzewskiego 89 Poznan60-568 Poland Instituto Politécnico de Lisboa-Instituto Superior de Engenharia de Lisboa Rua Conselheiro Emidio Navarro 1 Lisbon1959-007 Portugal Institute for Systems and Computer Engineering Technology and Science Rua Dr. Roberto Frias Porto4200-465 Portugal University of Trás-os-Montes e Alto Douro Quinta de Prados Vila Real5001-801 Portugal

Partnerships for Science Education (PAFSE) is a case study of project-based learning applied to real-world problems connected with public health and sustainable development. The EU-funded project organizes science education activities at low secondary level engaging students and school stakeholders (universities, research centres, start-ups, enterprises, governmental organisations, NGOs) in health promotion and disease prevention actions that benefit the health and well-being of the community. Projects leaded by schools are addressed within educational scenarios co-created with partners interested in STEM education and developed under a relevant public health issue through their continuous engagement in open schooling approach. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Project management

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